Topical product dispensing tool

ABSTRACT

A computer-implemented method can include receiving a first image input from a user, determining values of biological properties of the user, determining a first formulation, controlling a plurality of servomotors, receiving a second image input from a user, determining a second value of one of the biological properties of the user, comparing the first and second values, changing another of the values, determining a second formulation, and again controlling the plurality of servomotors. The image inputs can include images of the users skin. The values can be representative of a sensitivity of the user&#39;s skin to one or more materials and a current level of irritation of the user&#39;s skin. The formulations can contain a plurality of materials and can be configured to inhibit irritation of the user&#39;s skin based on the values of the biological properties of the skin.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/967,034, filed Dec. 11, 2015, which claims the benefit of U.S.Provisional Patent Application Ser. No. 62/090,733, filed on 11 Dec.2014, which are each hereby incorporated by reference in its entirety.

BACKGROUND 1. Field

The present disclosure relates to relates to an apparatus and method forformulating a customized personal care composition for a particularindividual.

2. Description of Related Prior Art

U.S. Pat. No. 7,349,857 discloses a process for formulating a customizedskin care product. The process involves determining an individual skinstructure and function at a point in time for the purpose of determiningand formulating skin care products that remedy the deficiencies observedin the skin. Objective and repeatable dermal biometric instrumentationtechniques can be used to measure skin moisture content, sebum content,firmness and elasticity properties, skin thickness, transepidermal waterloss, skin pH and to perform a photo analysis of the face with UV andvisible light. By customizing the skin care products, the individuallyadded active ingredients can be controlled, the diluents can bemodified, dermal penetration rates can be controlled, the surfactantsystems can be adjusted, and the stability of the product can becontrolled. To prevent the loss of active materials in the product, theskin care product is manufactured for an individual consumer and is onlysold in a quantity of a three months supply. Additionally, a variety ofingredients can be combined that a mass produced product cannot containdue to stability/compatibility issues.

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

SUMMARY

A computer-implemented method can include receiving, at a computingdevice having one or more processors, a first image input from a user,the first image input including a first image of a portion of the user'sskin; determining, at the computing device, a first value of a firstbiological property of the user's skin and a first value of a secondbiological property of the user's skin, both first values based at leastin part on the first image input, the first biological propertyrepresentative of a sensitivity of the user's skin to one or morematerials, the second biological property representative of a responseof the user's skin to one or more materials; determining, at thecomputing device, a first formulation containing a plurality ofmaterials, the first formulation based on the first value of the firstbiological property of the user's skin; receiving, at the computingdevice, a second image input from the user, the second image inputincluding a second image of the portion of the user's skin afterapplication of the first formulation containing the first quantities ofmaterials; determining, at the computing device, a second value of thesecond biological property of the user's skin based at least in part onthe second image input; comparing, at the computing device,respectively, the first value of the second biological property and thesecond value of the second biological property; updating, at thecomputing device, the first value of the first biological property ofthe user's skin to a second value of the first biological property ofthe user's skin based on said comparing; and determining, at thecomputing device, a second formulation containing the plurality ofmaterials based at least in part on the second value of the firstbiological property of the user's skin.

A computing device can comprise one or more processors; and anon-transitory, computer readable medium storing instructions that, whenexecuted by the one or more processors, cause the computing device toperform operations. The operations can comprise: receiving a first imageinput from a user, the first image input including a first image of aportion of the user's skin; determining a first value of a firstbiological property of the user's skin and a first value of a secondbiological property of the user's skin, both first values based at leastin part on the first image input, the first value representative of asensitivity of the user's skin to one or more materials, the secondvalue representative of a response of the user's skin; determining afirst formulation containing a plurality of materials, the firstformulation based on the first value of the first biological property ofthe user's skin; receiving after said controlling, a second image inputfrom the user, the second image input including a second image of theportion of the user's skin after application of the first formulationcontaining the first quantities of material; determining a second valueof the second biological property of the user's skin based at least inpart on the second image input; comparing respectively, the first valueof the second biological property and the second value of the secondbiological property; updating the first value of the first biologicalproperty of the user's skin to a second value of the first biologicalproperty of the user's skin based on said comparing; and determining asecond formulation containing the plurality of materials based at leastin part on the second value of the first biological property of theuser's skin.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description set forth below references the followingdrawings:

FIG. 1 is a functional block diagram of an exemplary computing deviceaccording to some implementations of the present disclosure;

FIG. 2 is a functional block diagram of an exemplary dispenser accordingto some implementations of the present disclosure;

FIG. 3 is a flow diagram of an example method according to someimplementations of the present disclosure; and

FIG. 4 is a simplified compartmental model.

DETAILED DESCRIPTION

The present disclosure, as demonstrated by the exemplary embodimentdescribed below, can provide a personal care product formulator with asensor or a sensor array and feedback analysis that measures and adjuststhe dose and composition of cosmeceutical or other agents in response tothe user reaction.

Personal care products can include skin care products, by way of exampleand not limitation. One or more embodiments of the present disclosurewill apply one or more sensors to detect biological features of a userand determine a formulation (cosmeceutical or other) that ispersonalized to that user's needs based on the sensed biologicalfeatures as well as any user input data. Exemplary biological featurescan be acne legion count, tissue inflammation, skin dryness, presence ofbacteria, extent and severity of sun damage or actinic keratosis, and/orskin oil level. The user input data can be data acquired through typing,voice, and/or uploading to a server.

The personalized formula will be mixed in a mixer operably communicatingwith the at least one sensor in one or more embodiments of the presentdisclosure. One or more embodiments of the present disclosure caninclude analytical software and can be configured for home use, ratherthan at the point of sale.

One or more embodiments of the present disclosure can include computersoftware containing a learning and updating algorithm based on Machinelearning or related algorithms that will learn the user's response todifferent applied products and adjust the formulations of futuredelivered products based on a desired response to the product on a dailybasis. Embodiments can sense and quantify values of the initial state ofthe user's skin, hair or other measured target of the personal careproduct prior to the application of the product as well as responsesafter the application of a known formulation. Measurements can be takenon a daily basis, rather than just once and product changes can occurdaily or as needed to adjust to skin oiliness, etc., and the individualresponse.

Data measured with a device according to one or more embodiments of thepresent disclosure can be communicated to a central database along withmeta-data entered by the user. Such data can include subjectiveimpressions of product efficacy for the purposes of conducting medicalresearch using both labeled and unlabeled data and deep learningalgorithms. This database can be used subsequently in the productformulation process, by establishing a cohort of users with similarfeatures and projected dose response or side effect curves.

Data gathering can be designed to be as controlled in the device fordetermining the formulation and the mixer. For example, data can becollected with only known variables changes and without any uncontrolledextraneous variable changes that could obfuscate the relationshipbetween dependent variables (outcomes) and independent variables (causalfactors) for any given response measurement. Any uncontrolled variablechanges should be considered and accounted for when interpreting themeaning of measurements of a user or a user response to any giventherapy in before and after measurements or in comparison to controlgroups. For example, background noise must be filtered out and placementof the sensor on the body must be accounted for as measurements of skinon the arm, for example, cannot be directly compared to skin on theface.

One or more embodiments of the present disclosure can include wearableor handheld sensors, such as electrodermal or multi-photon opticalsensors that may be placed in contact with a patch of skin. Such sensorsmay be used and data can be communicated wirelessly to the mixer andcontroller that will mix formulations of personal care products.

No feedback device is currently on the market to personalize a productafter the user has tried it, taking into account the skin's reaction tothe treatment plan. Additionally, many users of cosmeceutical productsfind that they must regularly alter their plan depending on the phase oftheir menstrual cycle, foods eaten, sleep and other unaccounted forinfluencing factors. No study regarding treatment plans that varycomponents of a skin care regime on a day to day basis exists. Finally,no product exists on the market that allows users to empirically “seefor themselves,” by using scientific measurements and allowing the userto track records of physical and psychological response to the treatmentplan, the results of the application of a skin care regime, away fromthe point of sale. This would be desirable to many users as a largeelement of distrust of the efficacy of cosmeceutical agents exits, dueto the lack of regulation and heavy marketing involved.

The response algorithm and concept may be applied to personal careproducts not related to skin, such as the saliva and various agents intoothpaste, including whitening agents, which, when properly applied canfight and prevent periodontal disease in the early stages. Hair careproducts, eye drops, deodorant and other products may also bepersonalized using one or more embodiments of the present disclosure.

FIG. 1 is a functional block diagram of an exemplary computing deviceaccording to some implementations of the present disclosure.Implementations of the present disclosure can include a computingdevice, such as an exemplary computing device 10. The computing device10 can be operated by a user 12. While a single computing device 10 andits associated user 12 and example components are described and referredto hereinafter, it should be appreciated that a computing deviceaccording to one or more implementations of the present disclosure canbe cooperatively defined by structures that are physically remote fromone another, such, for example, a server and smartphone. Examples of thecomputing device 10 include desktop computers, laptop computers, tabletcomputers, and mobile phones. In some embodiments, the computing device10 can be a mobile computing device associated with the user 12. In someembodiments, the computing device 10 can be a server, wherein input fromthe user 12 is received by the computing device 10 from anothercomputing device associated with the user 12.

The computing device 10 can include a communication device 14, aprocessor 16, and a memory 18. The computing device 10 can also includea camera 20 and a dispenser 22. By way of example and not limitation, inone or more implementations of the present disclosure, the camera 20 canbe a Point Grey Blackfly BFLY-U3-23S6C-C Machine Vision Camera withappropriate lensing, or possibly a pair of such cameras arranged forstereovision suitable for mapping the topography of the face in order todetermine the severity of lesions, sun damage, wrinkles and creases. Inother implementations of the present disclosure, a computing device caninclude other peripherals, such as a display (touch screen orotherwise), a mouse, a keyboard, and/or a microphone.

The communication device 14 is configured for communication between theprocessor 16 and other devices, e.g., a user's computing device, via anetwork 24. The network 24 can include a local area network (LAN), awide area network (WAN), e.g., the Internet, or a combination thereof.The communication device 14 can include any suitable communicationcomponents, such as a transceiver. Specifically, the communicationdevice 14 can transmit requests for data to other computing devices(such as exemplary server computing device 110) from the processor 16.The communication device 14 can transmit data and requests to processthe data to the computing device 110 from the processor 16. Thecommunication device 14 can provide response(s) to these requests to theprocessor 16. The communication device 14 can also provide systemupdates to the processor 16.

The memory 18 can be configured to store information at the computingdevice 10, such as skin conditions and topical product formulationscorrelated with respect to one another. The skin conditions stored inmemory 18 can include a data associated with the visual attributes ofsuch skin conditions so that images captured by the camera 20 can becorrelated to one or more of the skin conditions by the processor 16.The skin conditions stored in memory 18 can include a data associatedwith the compatibility of various materials held by the dispenser 22 sothat the materials dispensed by the dispenser 22 do not cause orexacerbate irritation of the skin of the user 12. Further, the skinconditions stored in memory 18 can include a data associated with thecompatibility of various materials held by the dispenser 22 so that thematerials dispensed by the dispenser 22 heal irritation and/or promotethe health of the skin of the user 12. The skin conditions stored inmemory 18 can include formulations or algorithms for derivingformulations for topical products.

Attributes of the user 12 can be stored in memory 18. For example, a“ghost” photo image can be stored in order to align future images toenhance consistency of the analysis. The skin tone and hue of the skincan be retained in memory 18. Various parameters that a physician mayfind relevant can be retained in memory 18, such as parameters that tendto limit a range of materials and material quantities that can beutilized safely by the user 12. Genetic information from the user 12 canalso be stored in memory 18.

Memory 18 can be defined in various ways in implementations of thepresent disclosure. Memory 18 can be any suitable storage medium (flash,hard disk, etc.). Memory 18 can be non-transitory in nature, and mayinclude volatile and non-volatile, and removable and non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules orother data. Memory 18 can further include RAM, ROM, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory or other solidstate memory technology, CD-ROM, digital versatile disks (DVD), or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and which can be accessed bythe controller 16. Memory 18 can store computer readable instructions,data structures or other program modules. By way of example, and notlimitation, communication media may include wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the abovemay also be included within the scope of computer readable media.

The processor 16 can be configured to control operation of the computingdevice 10. It should be appreciated that the term “processor” as usedherein can refer to both a single processor and two or more processorsoperating in a parallel or distributed architecture. The processor 16can operate under the control of an operating system, kernel and/orfirmware and can execute or otherwise rely upon various computersoftware applications, components, programs, objects, modules, datastructures, etc. Moreover, various applications, components, programs,objects, modules, etc. may also execute on one or more processors inanother computing device coupled to processor 16, e.g., in a distributedor client-server computing environment, whereby the processing requiredto implement the functions of embodiments of the present disclosure maybe allocated to multiple computers over the network 34. The processor 16can be configured to perform general functions including, but notlimited to, loading/executing an operating system of the computingdevice 10, controlling communication via the communication device 14,and controlling read/write operations at the memory 18. The processor 18can also be configured to perform specific functions relating to atleast a portion of the present disclosure including, but not limited to,loading/executing a topical product dispensing tool application and oneor more other applications at the computing device 10. By way of exampleand not limitation, in one or more implementations of the presentdisclosure, the processor 16 can be a multi-processor unit consisting ofone or more Nvidia Tesla K80 GPU accelerators used in concert with amain computing motherboard based on one or more Intel XEON Processor E7v3 chips.

FIG. 2 is a functional block diagram of an exemplary dispenser 22according to some implementations of the present disclosure. Thedispenser 22 can include a container 26 having a plurality of cavities28, 128, 228. Each of the cavities 28, 128, 228 can contain a respectivematerial of formulations for application on the skin of the user 12. Thedispenser 22 can also include a plurality of servomotors 30, 130, 230and a plurality of pumps 32, 132, 232. Each of the plurality ofservomotors 30, 130, 230 can be individually engaged with one of thepumps 32, 132, 232. For example, when the servomotor 30 is activated bythe processor 16, the servomotor 30 energizes and causes the pump 32 tourge material out of the cavity 28. The user 12 can mix the quantitiesof material dispensed by the dispenser 22 to produce the formulation andcan apply the formulation to his/her skin. By way of example and notlimitation, the following materials can be stored in one of the cavities28, 128, 228 in one or more implementations of the present disclosure:the three components A, B and C can be A oil/water emulsion (thevehicle), B can be a retinol microsponge formulation with a givenconcentration and C can be a 15% L-ascorbic acid (vitamin C)formulation.

By way of example and not limitation, in one or more implementations ofthe present disclosure, the dispenser 22 can be an arrangement ofsyringe pumps, such as the Warner Instruments Syringe Pump 11 Elite orthe AITECS 21S PLUS dual syringe pump or variations, or a single syringepump in combination with a carousel or linear array of syringecartridges which are aligned with the syringe pump through a positioningsystem (using, for example, a position-servo system like the Micromo2232S024BX4SAES-4096+22F 4:1, using magnetic absolute position encoderand brushless do motor with planetary gearhead in closed-loopoperation). In this case a single linear positioning system can beimplemented around a linear position servo, such as the Micromo2237S036CXR3965+BS22−1.5+HEDS5540106+BSA01+OPEC04, utilizing a brushlessDC motor with integrated ball screw and position feedback.

FIG. 3 is a flow diagram of an example method 34 according to someimplementations of the present disclosure. At 36, the computing device10 can receive a first image input from the user 12. The first imageinput including a first image of a portion of the skin of the user 12.The user 12 can capture the image with the camera 20. The image can bean infrared or ultraviolet image in one or more implementations of thepresent disclosure. The user 12 can be a subject in a research study, apatient of a dermatologist or family physician, or a consumer at home orin a mercantile. The user 12 can be a consumer desiring a sunscreen witha particular tint for a particular range of skin tones. The user 12 canbe a consumer desiring anti-aging products and in preventing sun damage,possibly by detecting subtle changes in skin tone (and having theirsunscreen tuned accordingly), ensuring the proper treatment of actinickeratosis (AK) (tuning their medicine accordingly, while managing sideeffects). The user 12 can be a subject of a cosmetic company study.

At 38, the computing device 10 can determine a first value of a firstbiological property of the skin of the user 12 and a first value of asecond biological property of the skin of the user 12. Both first valuescan be based at least in part on the first image input. The firstbiological property can be representative of a sensitivity of the skinof the user 12 to one or more materials in the dispenser 22. The secondbiological property can be representative of a current level ofirritation of the skin of the user 12.

By way of example and not limitation, in one or more implementations ofthe present disclosure, the processor 16 can apply Media CyberneticsImage-Pro Premier 3D to analyze the first image input and determine thefirst values. For example, processor 16, executing the Image-Prosoftware, can identify, classify and count lesions. Lesions can beclassified as comedones (open and closed), papules, pustules, nodules orcysts and localization. Features in the image can be identified asprimary and secondary lesions (scars). Holistic methods can be appliedby the processor 16, such as convolutional neural networks,semi-quantitative assessments of extent of acne using classificationmethods or possibly techniques from natural language processing.

At 40, the computing device 10 can determine a first formulationcontaining a plurality of materials. The first formulation can beconfigured to inhibit irritation of the user's skin based on the firstvalue of the first biological property of the user's skin. The firstformulation can also be configured to heal irritation of the user's skinbased on the first value of the second biological property of the user'sskin.

It is noted that the first values of the first and second biologicalproperties and/or the first formulation can be determined based onreference to data supplemental to the image data. One or moreimplementations of the present disclosure can utilize meta-data ordemographic data provided by the user 12 through an appropriate userinterface to determine the first and second biological properties and/orthe first formulation. One or more implementations of the presentdisclosure can utilize previously-stored user satisfaction data as wellas doctor-generated data. Such data can be stored in memory 18, ormemory 118 of computing device 110. Doctor-generated data can includedoctor preferences or user-adjusted severity ratings. Such data couldact as an over-ride to a formulation derived initially by the processor16. The image can be compared with other images stored in memory 18 ormemory 118. Machine learning algorithms can be applied based on latentvariables in memory 18 or memory 118. The processor 16, through thecommunication device 14, can communicate with the processor 116, throughthe communication device 114, to extract data from memory 118.Alternatively, the processor 16 can communicate a derived formulation tothe processor 116, the processor 116 can assess the formulation in viewof data stored in memory 118, and the processor 116 can communicateaffirmation of the formulation or recommend a revised formulation. Oneor more implementations of the present disclosure can store formulationsassociated with a particular user and/or a particular skin sample inmemory 18 or memory 118.

One or more implementations of the present disclosure can utilizepre-authorized formulations by a physician. For example, a proposed,first formulation can be directed to a physician for approval. If thephysician does not approve the proposed, first formulation, thecomputing device 10 can generate an alternative formulation forconsideration by the physician.

At 42, the computing device 10, through the processor 16, can controlthe plurality of servomotors 30, 130, 230 as necessary to dispenserespective first quantities of a plurality of materials from at leastsome of the cavities 28, 128, 228 with the pumps 32, 132, 232. Eachcavity can contain a respective material of the first formulation forapplication on the skin of the user 12.

The dispenser 22 could make use of automatic pumps and/or automaticmixing (automatic pumps with manual mixing may be best compromise) invarious implementations of the present disclosure. The container 26 cancontain active ingredients, moisturizers, tints, and scents. In one ormore implementations of the present disclosure, the dispenser 22 couldbe physically remote from other components of the computing device 10.For example, a dispenser could be positionable in an environment such asa bath tub or shower and communicate wirelessly with the processor 16.

Embodiments of the present disclosure can be applicable to a wide rangeof material dosing scenarios, including both prescription andnon-prescription drugs. Furthermore, as data is collected and knowledgeand understanding of the relationships between quantities described inmathematical relationships described herewithin are better understood byembodiments of the present disclosure, changes to the mathematicaldescriptions of the relationships between measured quantities and dosingoutcomes may be made to reflect the acquired data. This data andfeedback is a part of the desirability of embodiments of the presentdisclosure.

In one example, the three components A, B and C are A=oil/water emulsion(the vehicle), B is a retinol microsponge formulation with a givenconcentration and C is a 15% L-ascorbic acid (vitamin C) formulation. Itis noted that this example is for illustration purposes and that othercomponents can be utilized in one or more embodiments of the presentdisclosure. One or more embodiments of the present disclosure candetermine user-specific formulations that may be changed according touser response. For most cases, the following measurements can be made inthe current example, although it should be understood that as techniquesdevelop and become practical more measurements may be added to themeasured “response”. There are at least two categories of measurements:toxic response and therapeutic response.

“Toxic response” can be viewed as at least partially defining the secondbiological value during initial use of the embodiment and subsequentuses. Toxic response (hereafter T1, T2, T3, T4) can be measured asfollows. T1 can be a measure of Erythema made using cross polarizationspectroscopy wherein white LEDs and camera lenses can be used withpolarization filters oriented at ninety degrees. A material such as, forexample, the ThorLabs LPVISE2X2 triacetate, can be cut to theappropriate shape and dimensions for this purpose. The differencebetween the intensities of the red channel and green channels in eachpixel will be made over a photograph of the patch of skin taken usingcross polarization spectroscopy. The total magnitude of the differencein intensity of the blue channel and green channel will berepresentative of the amount of erythema (redness from inflammation)present in the skin. T1 can equal:Σ_(pixels)(red channel intensity value−green channel intensity value)

The equation above is one example of a quantitative measurement. Anotherexample of an equation for determining T1 is:Σ_(pixels)(red channel intensity value+pixels green channel intensityvalue)

The intensity value can be given by the camera on a standard 0-255intensity scale.

T2 can be a subjective input measure of the user experience of “burning”sensation of the skin on a scale from 0 to 10. One or more embodimentsof the present disclosure can include a user interface device such as akeyboard, keypad, or touch screen, for example, for the user to enterdata. If no input is received, it is assumed that the burning sensationhas a value of 0. Input may be made a keyboard or cell phone, forexample. T3 is a subjective input measure of the user experience of“stinging” sensation of the skin on a scale from 0 to 10. If no input isreceived, it is assumed that the burning sensation has a value of 0. T4is a measure of peeling: this may be determined by user input on asubjective scale of a rating from 1 through 10 or a severity rating maybe obtained using a camera, a pictographic database of images labelledwith severity of peeling and a deep learning algorithm, trained on thedatabase of peeling image data, which classifies the severity of thepeeling of the user via an image taken with the camera and fed to thedeep learning classifier as input. In some cases, peeling may beconsidered as a therapeutic response, for example, in the case ofexfoliation procedures.

“Therapeutic response” can be viewed as at least partially defining thesecond biological value during uses of the embodiment after the initialuse. Measurements of therapeutic response depend on the disorder to betreated. Some examples are given below, however, it is understood thatas sensor devices and measurement techniques improve, it may becomepractical to measure additional features of histological and clinicaltherapeutic response and adding or replacing those measurements listedbelow with new measurements to be tracked in a database and used to makedose adjustments at home based on data analysis concepts laid out hereshould be obvious to experts in the art or field.

For the anti-aging example worked here, therapeutic responsemeasurements may be:

“R1”—a measure of Transepidermal Water Loss. A transepidermal water losssensor, such as the Tewameter™ 300, by Courage-Khazaka Electronic GmbH,may be used.

“R2”—a measure of skin hydration. A corneometer, such as the CM 825 byCourage-Khazaka Electronic GmbH, may be used.

“R3”—a measure of wrinkle volume. This can be computed by the generationof facial topographies using 3D imaging techniques which includestereoscopic imaging, structured lighting techniques, laserrange-finding or light angulation as described by in WO2014047712.

“R4”—a measure of collagen production. This can be estimated using thecumulative reduction in wrinkle volume, scars, or textural improvementsof the face.

For users with acne:

“R1” can be a global severity rating found using deep learning methodsand image database to classify the severity of the user's acne from hisor her image captured using the camera device mentioned elsewhere inthis patent.

“R2” can be the number of open comedones.

“R3” can be the number of closed comedones.

“R4” can be the number of pustules.

“R5” can be the number of nodules.

“R6” can be the number of cysts.

R2-R6 can be evaluated using image segmentation with a sliding windowover the image, and by processing each segment with a trained machinelearning classifier such as a deep convolution neural network or otherarchitecture.

The response variable of greatest interest for determining dose in thecase of acne can be the global severity assessment (R1).

For users with Psorasis:

“R1” can be a severity rating of the plaque psoriasis. This can bedetermined from photographs of the sufferers' psoriasis, user inputregarding an estimate of the total percentage of skin covered bypsoriasis or, in some embodiments, an assessment of total coverage maybe based on total body imaging methodologies, and severity assessmentsmay also be made using the methodology of deep leaning described abovewith an algorithm trained on a database of labelled severityphotographs.

Other applications include treatments for Rosacea, Actinic Keratosis,sun damage and other ailments along with measures of the severity of thepathology similar to the above using either pictographic database ofseverity rating and deep learning, UV and IR imaging and sensing, imagesand image processing and related, appropriate, sensing methods todetermine the therapeutic response variables.

For Actinic Keratosis, retinol dosing would be replaced by dosing of5-Flourouracil, but the sensor based assessment of erythema and drugdosing algorithm would follow the same concept, beginning withconcentrations between 5% and 0.5%, depending on skin sensitivity.

The presence of metabolites of the active ingredients delivered to theskin may also be measured. In this example, the active ingredient to beindividually doses is retinoic acid, delivered in the form of a retinolmicrosponge solution.

Additional measurements can be taken by embodiments for deriving aninitial value for the first biological property and forrefining/revising the initial value. These measurements can includeheart rate and stress level as measured using Galvanic skin response.These can be measured for example using the Empatica E4 wristband fromEmpatica Inc. This may be used, for example, for psoriasis sufferers todetermine the extent to which stress predicts a breakout for a givensufferer by correlating increases of the skin response measured by theEmpatica device and the severity of the psoriasis, as measured above. Ifa pattern of correlation between increased stress followed by increasedpsoriasis breakout severity is noted, for example, a notice may be sentto the user (for example via Bluetooth® or an internet connection to acell phone) when predictive stress events occur and some topicaltreatment may be recommended in the appropriate dose for that user to beapplied as a preventative measure.

The first biological property can also be at least partially based ondata entered by a user. A standard questionnaire can be provided and theuser can be classified as having one of four skin types (for example)based on answers to the questionnaire: (a) Sensitive skin, (b) Dry Skin,(c) Normal Skin, or (d) Oily Skin. Other classifications can be appliedin one or more other embodiments of the present disclosure.

At 44, the computing device 10 can receive a second image input from theuser 12. The second image input including a second image of the portionof the skin of the user 12 after application of the first formulationcontaining the first quantities of materials. The second image input canbe in the same format as the first image input.

At 46, the computing device 10 can determine a second value of thesecond biological property of the user's skin based at least in part onthe second image input. At 48, the computing device 10 can compare thefirst value of the second biological property and the second value ofthe second biological property. For example, the computing device 10 candetermine if the skin of the user 12 evinces more or less irritationafter the application of the first formulation.

At 50, the computing device 10 can change the first value of the firstbiological property of the user's skin to a second value of the firstbiological property of the user's skin based on the comparison. In otherwords, at least one baseline value applied in deriving a formulation canbe changed in order to develop a subsequent formulation. This representsan application of feedback. The first value can be derived by applyingan algorithm that includes at least one coefficient having a preliminaryvalue. After the first formulation has been applied to the skin of theuser 12 and the image of the skin after application has been processed,at least one coefficient of the algorithm can be changed from thepreliminary value to a revised value. The first formulation can be basedstrictly on the first value of the first biological property of the skinof the user 12 but not based on the first value of the second biologicalproperty of the user's skin. In other words, the skin may not beirritated when the first formulation is derived. The second formulationcan be derived based at least in part on the second value of the firstbiological property (the revised value) and also based at least in parton the second value of the second biological property of the user's skin(the level of irritation).

One or more implementations of the present disclosure can storeformulations associated with a particular user and/or a particular skinsample, such as when a particular formulation used by a particular userand/or on skin having one or more particular attributes results ingreater irritation, less irritation, or no change in a level ofirritation. One or more implementations of the present disclosure cangenerate alerts or reports to a physician when a particular formulationused by a particular user and/or on skin having one or more particularattributes results in greater irritation, less irritation, or no changein a level of irritation. The reports can be configurable by thephysician as desired.

At 52, the computing device 10 can determine a second formulationcontaining the plurality of materials based at least in part on thesecond value of the first biological property of the skin of the user12. As set forth above, the second value of the first biologicalproperty can be different than the first value of the first biologicalproperty if the first formulation increased the level of irritation ofthe skin of the user 12. As with the first formulation, the computingdevice 10 can derive the second formulation based on data stored inmemory 118.

At 54, the computing device 10 can control the plurality of servomotors30, 130, 230 to dispense respective second quantities of the pluralityof materials from at least some of the cavities 28, 128, 228. At leastone of the second quantities of the plurality of materials is differentthan the corresponding quantity of the first quantities of the pluralityof materials. In other words, the second formulation is different fromthe first formulation in terms of the materials used and/or thequantities of particular materials used. The second quantities are basedon the second value of the first biological property of the skin of theuser 12. It is noted that if the first formulation produced reducedirritation in the skin of the user 12, the computing device 10 candispense the first formulation.

For sensitive skin and dry skin, as one example of the iterative,feedback process according to one or more embodiments of the presentdisclosure:

During days 0-14, the user can measure erythema using method T1 and alsoreports T2-T4, as well as inputting subjective data to an embodiment,such as satisfaction rating. Once this input is received, the embodimentcan initialize a formulation including, by percentage, 0.01% of retinol,applied in the morning or evening. Standard concentrations of retinolavailable in the non-prescription market today are: minimal 0.01%, to0.03%, 0.04% to 0.1% and stronger 0.5% to 1%.

Prior to the next dose (morning or evening), T1-T4 can be measured, aswell as satisfaction. If user is not satisfied, then a note of thethreshold irritation values recorded in T1-T4 is made and kept in memoryas the user-subjective tolerance threshold values. If the user is notsatisfied then the dose is reduced by 50%, if the user is veryunsatisfied, then the dose is reduced to 0 for the next application.Once the dose is reduced, the measures T1-T4 are tracked until the userindicates that they are satisfied and the change in time of the measuresT1-T4 are tracked.

If the user is satisfied, the dose is applied again after themeasurements are taken up to a maximum of 2 days. If the user is stillsatisfied and the irritation threshold has not been surpassed, the doseis increased by 0.01% (or if the user was very sensitive initially, thenthe dose will be increased by an amount equal to the highest tolerateddoes (for example 0.005%)) until the threshold irritation level isindicated.

Once the threshold irritation level is indicated and the values of T1-T4(with special attention to T1) are recorded, the dose of retinol can bedecreased by 50% f the user is not satisfied and reduced to 0 if theuser is very unsatisfied. The dose can then increased by 0.01% untilvalue of T1 or T2-T4 are just below the threshold values. This valuewill now be called the maximum tolerated irritation value. The maximumtolerated irritation value is that value of T1-T4 measured at the doselevel just below the dose level at which the user indicated thethreshold level of irritation had been reached. Note that the requireddose to reach the maximum tolerated irritation value need not be fixedand can change as the user acquires tolerance to the retinol treatment.

Once the maximum tolerated irritation value has been found, the retinoldose can be adjusted to the level that produced that value initially.The measurements T1-T4 will then be measured in the morning and at night(or just prior to applying the topical treatment) and the measurablechange over time will be recorded. If the value of T1-T4 falls below themaximum tolerated value to the value of the second highest toleratedvalue recorded, the dose will be increased by 0.01%, such that theirritation level remains at the maximum tolerated irritation level.

During days 14-30, if necessary, the response variables can be recorded,along with the time and dose and stored in a database. In someembodiments this phase may be shortened. After day 14-30: Adaptingstandard models from Dermatokinetics (Murthy, 2011) and Pharmacokinetics(Rosenbaum, 2011), the simplified compartmental model shown in FIG. 4 isassumed for the distribution of retinoic acid (and its related forms(Vourhees, 2014)) made available from the retinol the skin.

Microsponge technology is a patented technology of Advanced PolymerMaterials Inc. The following pharmacokinetic-pharmacodynamic model ofresponse, or a very similar equation, is adapted from a generalcompartmental model (CARUSO, 2009) involving a central compartment (heretaken as the epidermis) which receives a steady influx of drug dose(here modelled in R), an effect compartment and several compartmentsadjoining the central compartment (here only the dermis compartmentadjoins the central compartment). Both the toxic variables and responsevariables are assumed to follow the sigmoidal response curve adaptedfrom the above, however, in other embodiments and alternatives to thisresponse curve, such as hermetic responses, may be developed andapplied. In addition, moderate changes or development of these formulasmay be obvious to those skilled in the art. Thepharmacokinetic-pharmacodynamic model of response can be:

${Effect} = {{f(z)} = \frac{{Emax} \times \left( {k_{E\; 0}\mspace{14mu}\frac{z}{V\; 1}} \right)^{\gamma}}{\left( {k_{E\; 0}\mspace{14mu}\frac{z}{V\; 1}} \right)^{\gamma} + \left( {C\; 50} \right)^{\gamma}}}$

Where Effect may represent any one of T1, Ri and other continuousvariables, and interpolations of discrete variables Ri, Ti and where:

$z_{compliance} = {R\left\lbrack {{\frac{A_{E}}{\alpha_{E}k_{R\; 0}}\left( {1 - e^{{- k_{R\; 0}}t}} \right)} - {\frac{A_{E}}{\alpha_{E}\left( {k_{R\; 0} - \alpha_{E}} \right)}\left( {e^{{- \alpha_{E}}t} - e^{{- k_{R\; 0}}t}} \right)} + {\frac{A_{D}}{\alpha_{D}k_{R\; 0}}\left( {1 - e^{{- k_{R\; 0}}t}} \right)} - {\frac{A_{D}}{\alpha_{D}\left( {k_{R\; 0} - \alpha_{D}} \right)}\left( {e^{{- \alpha_{D}}t} - e^{{- k_{R\; 0}}t}} \right)}} \right\rbrack}$

It is approximated that R=r×dose. R is a rate of delivery of drug fromstratus corneum to the epidermis via a controlled releasemicrosponge/vehicle composition, which is approximated as a steady staterate proportional to the dose of retinol in the mixed product, withproportionality constant r.

Z is a consequence of the dermatokinetics of the retinoic acid (retinolderivative) in the skin and the parameters of Z are relevant for allpossible measured effects, E. A_(E) and A_(D) are parametersrepresenting the amount of retinoic acid in the “epidermis” and “dermis”compartments of the compartmental model, while α_(E) and α_(D) aretheoretical parameters related to the time decay dynamics of thedistribution of the retinol within the “epidermis” and “dermis”compartments. k_(R0) is a parameter that describes the dynamics of theevacuation of retinoic acid from the retinoic acid receptor compartment(equivalent to the “effect” compartment in pharmacodynamics models)either through metabolism and/or excretion. These parameters, and theproportionality constant, will vary depending on the individual. “t” isthe time over which a given dose is delivered, here it is set to 12hours, the time frame between applications of product at a given doseand over which a steady injection of retinol will be delivered from thecontrolled release microsphere emulsion. However, this simplifyingassumption may be relaxed in preferred embodiments. In this case thedifferential equations describing the system may be solved numerically,if necessary to make adjustments to the appropriate expressions for z interms of R in order to improve performance. If we assume that the rateof metabolism and transfer of the drug between compartments is fast onthe time scale of 12 hours over which the controlled release into theepidermis at rate R takes place. Thus:

$z_{compliance} \cong {r \times {dose} \times \left\lbrack {\frac{A_{E}}{\alpha_{E}k_{R\; 0}} + \frac{A_{D}}{\alpha_{D}k_{R\; 0}}} \right\rbrack}$

For non-compliance it is assumed that:

$z_{{non}\text{-}{compliance}} \cong {{\left\lbrack {\frac{A_{E}}{\alpha_{E}k_{R\; 0}} + \frac{A_{D}}{\alpha_{D}k_{R\; 0}}} \right\rbrack \times \frac{e^{{- k_{R\; 0}}t^{\prime}}}{k_{ER}}} + {r \times {dose}_{{last}\mspace{14mu}{application}} \times {\quad\left\lbrack {\frac{A_{E}}{\alpha_{E}\left( {k_{R\; 0} - \alpha_{E}} \right)}\left( {1 - e^{{- \alpha_{E}}T_{compliance}}} \right){\left( {e^{{- \alpha_{E}}t^{\prime}} - e^{{- k_{R\; 0}}t^{\prime}}} \right)++}\frac{A_{D}}{\alpha_{D}\left( {k_{R\; 0} - \alpha_{D}} \right)}\left( {1 - e^{{- \alpha_{D}}T_{compliance}}} \right)\left( {e^{{- \alpha_{D}}t^{\prime}} - e^{{- k_{R\; 0}}t^{\prime}}} \right)} \right\rbrack}}}$

It follows that an adjustment parameter to z as a function of changingapplied dose is derived from the time dynamics of z by solving theappropriate differential equations (CARUSO, 2009) to account for changesin the steady state rate of drug delivery, R, into the epidermis as theapplied dose changes.

V1 is a parameter related to the theoretical volume of the “epidermis”compartment. This will vary from individual to individual.T_(compliance) is the time over which the user was compliant withapplication and t′ is the time since last compliance (productapplication).

The parameters V1 and those comprising the z quantity are the same forall measured effects, both toxic and therapeutic. k_(E0) is a parameterrepresenting the elimination rate of the retinoic acid (or relatedretinol and esters) from the “epidermis” compartment.

For each effect Ri and Ti the following parameters must be uniquelydetermined: the parameter γ is a measure of the sigmoldicity of thepharmacodynamics of the response to the retinoic acid dose resulting inthe given effect, E_(max) is the maximum pharmacodynamics response for agiven effect and C50 is a parameter representing the concentration ofdrug in the “epidermis” compartment at which 50% of the maximum effectis achieved in theory.

C50 will change with time for toxic response as the user is desensitizedto the toxic effects (Rosenbaum, 2011), specifically effects T1-T4. Amodel for C50 with the appropriate properties is:

${C\; 50} = \frac{C\; 5\mspace{14mu} Q_{o}}{1 + {\left( {\frac{C\; 5\mspace{14mu} Q_{o0}}{C\; 5\mspace{14mu} Q_{0}} - 1} \right)e^{- \frac{\tau}{T_{\max}}}}}$

Where C50_(∞) represents the long term sensitivity of the user to thetoxic effect after desensitization, C50_(∞) is the initial sensitivityand T_(max) is a parameter related to the speed at which the user isdesensitized to the toxic effect. The independent variable .tau. is thetime the user has been applying the product diligently. A correction toC50 is applied for non-compliance such that τ is equal to the greater ofthe number of days of compliance minus the number of days ofnon-compliance or 0, or a measured model of non-compliance effects basedon the user's measured irritation after non-compliance.

Thus there are 9 general pharmacokinetic parameters and 5 effectspecific parameters that must be fit to the measured individual responseas a function of dose. Given the first 14-30 days of data measurementsfrom an individual user, these parameters may be fit using a standardmathematical software such as Mathematica.

Once these parameters have been found for an individual, thepersonalized dose is (when the assumption that the rate of metabolismand transfer of the drug between compartments is fast on the time scaleof 12 hours over which the controlled release into the epidermis at rateR takes place:

${{Dose}(\tau)} = {{f^{1}\left( {{{Effect} = {{maximum}\mspace{14mu}{tolerated}\mspace{14mu}{irritation}}},\tau} \right)}\text{/}\left( {r\mspace{14mu}\left\lbrack {\frac{A_{E}}{\alpha_{E}k_{R\; 0}} + \frac{A_{D}}{\alpha_{D}k_{R\; 0}}} \right\rbrack} \right)}$

Where

$r\left\lbrack {\frac{A_{E}}{\alpha_{E}k_{R\; 0}} + \frac{A_{D}}{\alpha_{D}k_{R\; 0}}} \right\rbrack$are found from fitting the measured effects, Effects=f(z), to theindependent variable of the measured dose (from the mixer dispenser) andmeasured response data (as described in T1-T4, above) using the formulasfor z_compliance and z_(non-compliance) as a function of dose asappropriate.

The materials retinol microsponge solution, oil and water vehicle and15% L-ascorbic acid solutions and then combined in the mixer such thatthe concentration of the retinol microsponge solution is equal toDose(τ).

Desired response effects will be reported to the user. This is expectedto encourage compliance. As more data is collected from more users, theinitial applied dose during days 0-14 will be adjusted to reflect thedose-response behavior of past users in the database that are identifiedto be similar to the user based on initial measurements.

It is noted that in other implementations, other forms of input can bedirected to a computing device to derive formulations for application onthe skin of the user 12. Electro-dermal measurement can be utilized, forexample. Further, the user 12 can enter demographic data, such as scentpreference or tint preference.

In one or more implementations of the present disclosure, dataassociated with the particular user 12 can utilized by a community ofusers. The data associated with the particular user 12 can be anonymizedand stored in memory 118. Through the sharing of data and machinelearning principles, one or more implementations of the presentdisclosure can increasingly define a reliable global severity ratingscale using deep learning techniques by identifying latent variablesdetectable in images of skin. Further, such implementations can definefacilitate telemedicine and automated dosing.

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known procedures,well-known device structures, and well-known technologies are notdescribed in detail.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The term “and/or” includes any and all combinations of one ormore of the associated listed items. The terms “comprises,”“comprising,” “including,” and “having,” are inclusive and thereforespecify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. The method steps,processes, and operations described herein are not to be construed asnecessarily requiring their performance in the particular orderdiscussed or illustrated, unless specifically identified as an order ofperformance. It is also to be understood that additional or alternativesteps may be employed.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms when used herein do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments.

The techniques described herein may be implemented by one or morecomputer programs executed by one or more processors. The computerprograms include processor-executable instructions that are stored on anon-transitory tangible computer readable medium. The computer programsmay also include stored data. Non-limiting examples of thenon-transitory tangible computer readable medium are nonvolatile memory,magnetic storage, and optical storage.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a tangible computer readable storagemedium, such as, but is not limited to, any type of disk includingfloppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs), EPROMs, EEPROMs,magnetic or optical cards, application specific integrated circuits(ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatuses to perform the required method steps. Therequired structure for a variety of these systems will be apparent tothose of skill in the art, along with equivalent variations. Inaddition, the present disclosure is not described with reference to anyparticular programming language. It is appreciated that a variety ofprogramming languages may be used to implement the teachings of thepresent disclosure as described herein, and any references to specificlanguages are provided for disclosure of enablement and best mode of thepresent invention.

The present disclosure is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

While the present disclosure has been described with reference to anexemplary embodiment, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the presentdisclosure. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from the essential scope thereof.Therefore, it is intended that the present disclosure not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this present disclosure, but that the present disclosurewill include all embodiments falling within the scope of the appendedclaims. The right to claim elements and/or sub-combinations that aredisclosed herein as other present disclosures in other patent documentsis hereby unconditionally reserved.

We claim:
 1. A computer-implemented method comprising: receiving, at acomputing device having one or more processors, a first image input froma user, the first image input including a first image of a portion ofthe user's skin; determining, at the computing device, a first value ofa first biological property of the user's skin and a first value of asecond biological property of the user's skin, both first values basedat least in part on the first image input, the first biological propertyrepresentative of a sensitivity of the user's skin to one or morematerials, the second biological property representative of a responseof the user's skin to one or more materials; determining, at thecomputing device, a first formulation containing a plurality ofmaterials, the first formulation based on the first value of the firstbiological property of the user's skin; receiving, at the computingdevice, a second image input from the user, the second image inputincluding a second image of the portion of the user's skin afterapplication of the first formulation containing the first quantities ofmaterials; determining, at the computing device, a second value of thesecond biological property of the user's skin based at least in part onthe second image input; comparing, at the computing device,respectively, the first value of the second biological property and thesecond value of the second biological property; updating, at thecomputing device, the first value of the first biological property ofthe user's skin to a second value of the first biological property ofthe user's skin based on said comparing; and determining, at thecomputing device, a second formulation containing the plurality ofmaterials based at least in part on the second value of the firstbiological property of the user's skin.
 2. The computer implementedmethod of claim 1 wherein the response is a therapeutic response of theuser's skin to one or more materials.
 3. The computer implemented methodof claim 1 wherein the response is a toxic response.
 4. The computerimplemented method of claim 3 wherein the first formulation isconfigured to inhibit irritation of the user's skin based on the firstvalue of the first biological property of the user's skin.
 5. Thecomputer implemented method of claim 4 wherein said determining thefirst value of the first biological property of the user's skin isfurther defined as: determining, at the computing device, the firstvalue of the first biological property of the user's skin by applying analgorithm including at least one coefficient having a preliminary value.6. The computer implemented method of claim 5 wherein said determiningthe second value of the first biological property of the user's skin isfurther defined as: updating, at the computing device, the at least onecoefficient from the preliminary value to a revised value.
 7. Thecomputer implemented method of claim 4 wherein said determining thefirst formulation is further defined as: determining, at the computingdevice, the first formulation containing a plurality of materials basedon the first value of the first biological property of the user's skinbut not based on the first value of the second biological property ofthe user's skin.
 8. The computer implemented method of claim 7 whereinsaid determining the second formulation is further defined as:determining, at the computing device, the second formulation containingthe plurality of materials based at least in part on the second value ofthe first biological property of the user's skin and also based at leastin part on the second value of the second biological property of theuser's skin.
 9. The computer implemented method of claim 4 wherein saiddetermining the first value of the first biological property of theuser's skin is further defined as: determining, at the computing device,the first value of the first biological property of the user's skin by,at least in part, counting a number of lesions in the first image. 10.The computer implemented method of claim 4 wherein said receiving thefirst image input is further defined as: receiving, at the computingdevice, the first image input from a user, the first image inputincluding a first image of a portion of the user's skin and being aninfrared image.
 11. The computer implemented method of claim 4 whereinsaid receiving the first image input is further defined as: receiving,at the computing device, the first image input from a user, the firstimage input including a first image of a portion of the user's skin andbeing an ultraviolet image.
 12. The computer implemented method of claim4 further comprising: retrieving, by the computing device, data from asecondary memory physically remote from the computing device through anetwork during said determining the first formulation.
 13. The computerimplemented method of claim 12 wherein the data is associated with oneof the user and others.
 14. A computing device comprising: one or moreprocessors; and a non-transitory, computer readable medium storinginstructions that, when executed by the one or more processors, causethe computing device to perform operations comprising: receiving a firstimage input from a user, the first image input including a first imageof a portion of the user's skin; determining a first value of a firstbiological property of the user's skin and a first value of a secondbiological property of the user's skin, both first values based at leastin part on the first image input, the first value representative of asensitivity of the user's skin to one or more materials, the secondvalue representative of a response of the user's skin; determining afirst formulation containing a plurality of materials, the firstformulation based on the first value of the first biological property ofthe user's skin; receiving a second image input from the user, thesecond image input including a second image of the portion of the user'sskin after application of the first formulation containing the firstquantities of material; determining a second value of the secondbiological property of the user's skin based at least in part on thesecond image input; comparing respectively, the first value of thesecond biological property and the second value of the second biologicalproperty; updating the first value of the first biological property ofthe user's skin to a second value of the first biological property ofthe user's skin based on said comparing; and determining a secondformulation containing the plurality of materials based at least in parton the second value of the first biological property of the user's skin.15. The computing device of claim 14 wherein the response is atherapeutic response of the user's skin to one or more materials. 16.The computing device of claim 14 wherein the response is a toxicresponse.
 17. The computing device of claim 16 wherein the firstformulation is configured to inhibit irritation of the user's skin basedon the first value of the first biological property of the user's skin.18. The computing device of claim 17 wherein the determining of thefirst value of the first biological property of the user's skin isfurther defined as determining the first value of the first biologicalproperty of the user's skin by applying an algorithm including at leastone coefficient having a preliminary value.
 19. The computing device ofclaim 18 wherein the determining of the second value of the firstbiological property of the user's skin is further defined as updatingthe at least one coefficient from the preliminary value to a revisedvalue.
 20. The computing device of claim 17 wherein the determining ofthe first formulation is further defined as determining the firstformulation containing a plurality of materials based on the first valueof the first biological property of the user's skin but not based on thefirst value of the second biological property of the user's skin. 21.The computing device of claim 20 wherein the determining of the secondformulation is further defined as determining the second formulationcontaining the plurality of materials based at least in part on thesecond value of the first biological property of the user's skin andalso based at least in part on the second value of the second biologicalproperty of the user's skin.
 22. The computing device of claim 17wherein the determining of the first value of the first biologicalproperty of the user's skin is further defined as determining the firstvalue of the first biological property of the user's skin by, at leastin part, counting a number of lesions in the first image.
 23. Thecomputing device of claim 17 wherein the receiving of the first imageinput is further defined as receiving the first image input from a user,the first image input including a first image of a portion of the user'sskin and being an infrared image.
 24. The computing device of claim 17wherein the receiving of the first image input is further defined asreceiving the first image input from a user, the first image inputincluding a first image of a portion of the user's skin and being anultraviolet image.
 25. The computing device of claim 17 wherein saidnon-transitory, computer readable medium storing instructions furthercauses the computing device to perform an operation comprising:retrieving data from a secondary memory physically remote from thecomputing device through a network during said determining the firstformulation.
 26. The computing device of claim 25 wherein the data isassociated with one of the user and others.
 27. A computer-implementedmethod comprising: receiving, at a computing device having one or moreprocessors, a first image input from a user, the first image inputincluding a first image of a portion of the user's skin; determining, atthe computing device, a first value of a first biological property ofthe user's skin and a first value of a second biological property of theuser's skin, both first values based at least in part on the first imageinput, the first biological property representative of a sensitivity ofthe user's skin to one or more materials, the second biological propertyrepresentative of a response of the user's skin; determining, at thecomputing device, a first formulation containing a plurality ofmaterials, the first formulation based on the first value of the firstbiological property of the user's skin; transmitting, at the computingdevice, the first formulation; receiving, at the computing device, aftersaid transmitting, a second image input from the user, the second imageinput including a second image of the portion of the user's skin afterapplication of the first formulation containing the first quantities ofmaterials; determining, at the computing device, a second value of thesecond biological property of the user's skin based at least in part onthe second image input; comparing, at the computing device,respectively, the first value of the second biological property and thesecond value of the second biological property; updating, at thecomputing device, the first value of the first biological property ofthe user's skin to a second value of the first biological property ofthe user's skin based on said comparing; determining, at the computingdevice, a second formulation containing the plurality of materials basedat least in part on the second value of the first biological property ofthe user's skin; and transmitting, at the computing device, the secondformulation.
 28. The computer implemented method of claim 1, wherein atleast one of the first formulation and the second formulation isdetermined based at least in part on data received from at least one ofa wearable sensor and a handheld sensor.
 29. The computer implementedmethod of claim 28, wherein at least one of the wearable sensor and ahandheld sensor includes a multi-photon optical sensor.
 30. The computerimplemented method of claim 1, wherein at least one of the firstformulation and the second formulation is determined based at least inpart on data stored in a database, wherein the database includes acohort of users with similar features and projected dose response. 31.The computer implemented method of claim 30, wherein the data stored inthe database includes demographic data, user satisfaction data ordoctor-generated data.
 32. The computer implemented method of claim 30,wherein at least one of the first formulation and the second formulationis determined based at least in part on application of machine learningprinciples to the data stored in the database.
 33. The computerimplemented method of claim 1, wherein the second formulation is furtherdetermined based at least in part on user satisfaction data.
 34. Thecomputer implemented method of claim 1, wherein the second formulationis further determined based at least in part on machine learningalgorithms applied to at least one of the first image input and thesecond image input.
 35. The computing device of claim 27 wherein theresponse is a therapeutic response of the user's skin to one or morematerials.
 36. The computing device of claim 27 wherein the response isa toxic response.
 37. The computing device of claim 36 wherein the firstformulation is configured to inhibit irritation of the user's skin basedon the first value of the first biological property of the user's skin.